人工智能在材料科学与工程中的应用：当前状况、关键挑战与未来轨迹
Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectorie
AI总结：
本文探讨人工智能在材料科学与工程中的应用现状、挑战及未来发展方向，涵盖机器学习方法、数据重要性及关键挑战。
摘要：
人工智能正迅速改变材料科学与工程，提供强大的工具来应对复杂性、加速发现并优化材料设计，这些在以前是无法实现的。随着算法进步的加快和数据可用性的增加，人工智能正成为材料研究人员的重要能力。本文综述了当前的全面且结构化的现状，整合了最近的进展和方法，为寻求有效利用这些数据驱动技术的材料科学家提供参考。我们调查了机器学习方法的整个范围，从传统算法到先进的深度学习架构，包括卷积神经网络、图神经网络和变换器，以及新兴的生成式人工智能和概率模型，如高斯过程用于不确定性量化。本文还探讨了数据在该领域中的关键作用，强调了有效的表示和特征化策略，涵盖组成、结构、图像基于和语言启发的方法，结合适当的预处理，从根本上决定了机器学习模型在材料研究中的性能。持续的数据质量、数量和标准化相关挑战，这些对材料科学和工程中的模型开发和应用有重大影响，也得到了讨论。
英文摘要：
Artificial Intelligence is rapidly transforming materials science and engineering, offering powerful tools to navigate complexity, accelerate discovery, and optimize material design in ways previously unattainable. Driven by the accelerating pace of algorithmic advancements and increasing data availability, AI is becoming an essential competency for materials researchers. This review provides a comprehensive and structured overview of the current landscape, synthesizing recent advancements and methodologies for materials scientists seeking to effectively leverage these data-driven techniques. We survey the spectrum of machine learning approaches, from traditional algorithms to advanced deep learning architectures, including CNNs, GNNs, and Transformers, alongside emerging generative AI and probabilistic models such as Gaussian Processes for uncertainty quantification. The review also examines the pivotal role of data in this field, emphasizing how effective representation and featurization strategies, spanning compositional, structural, image-based, and language-inspired approaches, combined with appropriate preprocessing, fundamentally underpin the performance of machine learning models in materials research. Persistent challenges related to data quality, quantity, and standardization, which critically impact model development and application in materials science and engineering, are also addressed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
